In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Ex Input:
Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: How is a "chunk of posts" defined in this work?

Ex Output:
chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account


Ex Input:
 Models include both neural nets (e.g. RNNs, CNNs) and standard machine learning tools (e.g. Naive Bayes with Laplace Smoothing, k-clustering, SVM with linear kernel).  
 Question: Which machine learning models are used?

Ex Output:
RNNs CNNs Naive Bayes with Laplace Smoothing k-clustering SVM with linear kernel


Ex Input:
Our goal is to decouple the content selection from the decoder by introducing an extra content selector. The most intuitive way is training the content selector to target some heuristically extracted contents. For example, we can train the selector to select overlapped words between the source and target BIBREF6 , sentences with higher tf-idf scores BIBREF20 or identified image objects that appear in the caption BIBREF21 . INLINEFORM0 as Latent Variable: Another way is to treat INLINEFORM1 as a latent variable and co-train selector and generator by maximizing the marginal data likelihood. By doing so, the selector has the potential to automatically explore optimal selecting strategies best fit for the corresponding generator component. Reinforce-select (RS) BIBREF24 , BIBREF9 utilizes reinforcement learning to approximate the marginal likelihood. We propose Variational Reinforce-Select (VRS) which applies variational inference BIBREF10 for variance reduction. 
 Question: How is the model trained to do only content selection?

Ex Output:

target some heuristically extracted contents treat INLINEFORM1 as a latent variable and co-train selector and generator by maximizing the marginal data likelihood reinforcement learning to approximate the marginal likelihood  Variational Reinforce-Select (VRS) which applies variational inference BIBREF10 for variance reduction


